class_docstr: Hand-coded policy that returns random actions.
class_docstr_tok: ["Hand-coded","policy","that","returns","random","actions","."]
code: def init_view_requirements(self):
      super().init_view_requirements()
      # Disable for_training and action attributes for SampleBatch.INFOS column
      # since it can not be properly batched.
      vr = self.view_requirements[SampleBatch.INFOS]
      vr.used_for_training = False
      vr.used_for_compute_actions = False
  
file_path: ray/python/ray/rllib/examples/policy/random_policy.py
imports: from gymnasium.spaces import Box
  import numpy
  import random
  import tree
  from typing import List, Optional, Union
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.typing import ModelWeights, TensorStructType, TensorType
label_desc: Use image node_id 2 for calling the RandomPolicy obj's underlying member method code with example usage: obj.init_view_requirements() without return types